在传统各向异性扩散滤波算法的基础上，提出了一种自适应梯度阈值各向异性滤波算法，用于有效地抑制红外复杂背景、滤除噪声，同时增强红外弱小目标。该算法根据图像的局部特性，利用其在不同方向上的梯度特点，判断某点像素是噪声还是图像以及其存在于图像的平滑区域还是边缘区域。 文中据此提出了自适应求取边缘函数中的梯度阈值(K值)的方法，解决了原各向异性滤波算法的边缘函数中K值固定单一的问题。实验证明: 与原各向异性滤波算法和其他背景抑制算法相比，提出的算法增加了去噪功能,对各种复杂背景抑制效果更好，增强后的图像信噪比提高了近2倍。

On the basis of traditional anisotropic diffusion filtering algorithm, an adaptive gradient threshold based anisotropic filtering algorithm is proposed to suppress the infrared complex background, filter out the noise effectively and enhance the infrared dim target. According to the local features of a image and its gradient features in different directions , the algorithm can determine the point of a pixel to be a noise or an image, and can point out the point of the pixel to be in the smooth region or the edge region. Based on above, it proposes a method to calculate the gradient threshold (K value) of the edge function adaptively and to solve the shortcoming that the K value is fixed and single in the edge function of the traditionall anisotropic filtering algorithm. This experiment shows that this algorithm increases the denoising function, suppresses complex background effectively, and enhances Signal to Noise Radio(SNR) of the image by 2 times as compared with that of the original anisotropic filtering algorithm and other background suppression algorithms.